VBASR: The Vision System V ision B ased A utonomous S ecurity R obot Bradley University – ECE Department Senior Capstone Project Sponsored by Northrup Grumman May 04, 2010 Student: Kevin Farney Advisor: Dr. Joel Schipper
Presentation Outline What the project is… What has been completed… Results… 2
Project Summary What is VBASR? Autonomous, Mobile, Security Camera VBASR is a computer vision project Primary Goals – Using Computer Vision Navigation Obstacle Avoidance 3
Vision Algorithm System Block Diagram 4
The Platform Hardware iRobot Create Webcam Software OpenCV2.0 5
Vision Algorithm – Idea #1 6
Vision Algorithm – Idea #2 7
Vision Algorithm – Idea #3 8
Vision Algorithm – High Level 9
Vision Algorithm – Detailed Feature Extraction 10
Feature Extraction 11
Testing OpenCV - Filters 12
Testing OpenCV - Filters 13
Testing OpenCV - Filters 14
Feature Extraction 15
Testing OpenCV - Edge 16
Why Filters? Noise Reduction 17
Feature Extraction 18
Testing OpenCV - Corners 19
Feature Extraction 20
Testing OpenCV – Flood Fill 21
Vision Algorithm – Detailed Feature Lines Algorithm Extraction 22
Lines Algorithm Feature Extraction 23
Lines Algorithm 24
Vision Algorithm – Detailed Corners Algorithm 25
Corners Algorithm Feature Extraction 26
Corners Algorithm 27
Vision Algorithm – Detailed Colors Algorithm 28
Colors Algorithm Feature Extraction 29
Colors Algorithm 30
Vision Algorithm – Detailed 31
Vision Algorithm - Example One 32
Vision Algorithm - Example One 33
Vision Algorithm - Example One 34
Vision Algorithm - Example One 35
Vision Algorithm - Example One 36
Vision Algorithm - Example Two 37
Vision Algorithm - Example Two 38
Vision Algorithm - Example Two 39
Vision Algorithm - Example Two 40
Vision Algorithm - Example Two 41
Quantitative Results 42
Qualitative Results Initial testing yields promising results! Two programs ran independently Vision system iRobot controls Verified quantitative results Exceeded expectations 43
Questions? VBASR by Kevin Farney 44
References Sage, K., and S. Young. "Security Applications of Computer Vision." IEEE Transactions on Aerospace and Electronic Systems 14.4 (1999): 19-29. Aug. 2002. DeSouza, G. N., and A. C. Kak. "Vision for Mobile Robot Navigation: A Survey." IEEE Transactions on Pattern Analysis and Machine Intelligence 24.2 (2002): 237-67. Aug. 2002. Davies, E. R. Machine Vision: Theory, Algorithms, Practicalities . San Francisco: Morgan Kaufmann, 2005. Forsyth, D., and J. Ponce. Computer Vision: a Modern Approach. Upper Saddle River, N.J.: Prentice Hall, 2003. Shapiro, Linda G., and George C. Stockman. Computer Vision . Upper Saddle River, NJ: Prentice Hall, 2001. 45
References Scott, D., and F. Aghdasi. "Mobile Robot Navigation In Unstructured Environments Using Machine Vision." IEEE AFRICON 1 (1999): 123-26. Aug. 2002. Argyros, A. A., and F. Bergholm. "Combining Central and Peripheral Vision for Reactive Robot Navigation." IEEE CSC Computer Vision and Pattern Recognition 2 (1999): 646-51. Aug. 2002. Shimizu, S., T. Kato, Y. Ocmula, and R. Suematu. "Wide Angle Vision Sensor with Fovea-navigation of Mobile Robot Based on Cooperation between Central Vision and Peripheral Vision." IEEE/RSJ Intelligent Robots and Systems 2 (2001): 764- 71. Aug. 2002. Matsumoto, Y., K. Ikeda, M. Inaba, and H. Inoue. "Visual Navigation Using Omnidirectional View Sequence." IEEE/RSJ Intelligent Robots and Systems 1 (1999): 317-22. Aug. 2002. Orghidan, R., J. Salvi, and E. M. Mouaddib. "Accuracy Estimation of a New Omnidirectional 3D Vision Sensor." IEEE/ICIP Image Processing 3 (2005): 365- 68. Mar. 2006. 46
References Kosinski, R. J. "Literature Review on Reaction Time." Clemson University, Aug. 2009. 10 Nov. 2009. <http://biae.clemson.edu/bpc/bp/Lab/110/reaction.htm> Canny, J. "A Computational Approach to Edge Detection." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8.6 (1986): 679-98. Jan. 2009. Shi, W., and J. Samarabandu. "CORRIDOR LINE DETECTION FOR VISION BASED INDOOR ROBOT NAVIGATION." IEEE CCECE (2006): 1988-991. Jan. 2007. Marques, C., and P. Lima. "Multisensor Navigation for Nonholonomic Robots in Cluttered Environments." IEEE Transactions on Robotics and Automation 11.3 (2004): 70-82. Oct. 2004. Ohya, I., A. Kosaka, and A. Kak. "Vision-Based Navigation by a Mobile Robot with Obstacle Avoidance Using Single-Camera Vision and Ultrasonic Sensing." IEEE Transactions on Robotics and Automation 14.6 (1998): 969-78. Aug. 2002. 47
Quantitative Results 48
Selecting Parameter Values 49
Lines Algorithm - Problems 50
Corners Algorithm - Problems 51
Colors Algorithm - Problems 52
Colors Algorithm - Solution 53
Filters - Normal Normal Blur Normalized box filter – summation of pixels over a neighborhood 54
Filters – Gaussian Gaussian Blur Convolution of source image with specified gaussian kernel Matrix of ksize (parameter) x 1 with filter coefficients: = 55
Filters Median Blur Returns median of pixel neighborhood into the destination image for each pixel 56
Canny Edge Detection Implements Canny Algorithm First noise-reduction needed (filters) Intensity Gradients 8 points Non-Maximum Suppression Hysteresis Thresholding High – discards noisy pixels Low – connects the edges into lines (binary) 57
Corner Detection Good Features To Track Calculates minimal eigenvalue per pixel Covariation Matrix of derivatives Then eigenvalues represent corners Non-maxima suppression (3x3 pixels) Rejection by quality level (parameter) qualityLevel•max(eigImage(x,y)) Rejection by distance (parameter) 58
Price Breakdown iRobot Create Premium Development Package $299 Pioneer 3-DX upwards of $5000 Microsoft Robotics Developers Studio R2 free download Visual Studio 2008 $500 and up Visual C# editor – free download Small Netbook Looking for around $300 59
Microsoft Robotics Developer Studio CCR (Concurrency and Coordination Runtime) DSS (Decentralized Software Services) VPL (Visual Programming Language) VSE (Visual Simulation Environment) 60
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